Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for calibrating a model of a complex flow system in which a substance flows from one point to another, comprising: collecting by an embedded sensor an observed value for a node of a plurality of nodes in the complex flow system, wherein each of the plurality of nodes are points in the complex flow system at which a measurement of the substance is determined within the model; making a comparison between an output from the model and the observed value, wherein the output and the observed value represent a parameter that affects flow of the substance within the complex flow system at the node; computing an adjoint sensitivity for the node based on the comparison; and adjusting a set of coefficients of the model associated with the node based on the adjoint sensitivity in order to bring the output from the model closer to the observed value.
This invention relates to calibrating models of complex flow systems where a substance moves from one point to another. The problem addressed is ensuring accurate model predictions by aligning simulated outputs with real-world measurements. The method involves using embedded sensors to collect observed values at specific nodes within the system, where each node represents a point where the substance's properties are measured. The model generates an output for the same parameter at that node, and a comparison is made between the observed value and the model's output. An adjoint sensitivity is then computed based on this comparison, which quantifies how changes in model coefficients affect the output. The model's coefficients are adjusted using this sensitivity to minimize the discrepancy between the model and observed data. This iterative process improves the model's accuracy by refining its parameters to better match real-world conditions. The approach is particularly useful in systems where precise flow predictions are critical, such as fluid dynamics, chemical processes, or environmental modeling.
2. The method of claim 1 , wherein the embedded sensor is configured to collect the observed value for transmission to a computer device performing the method.
A system and method for monitoring and controlling industrial processes involves embedding sensors within a process environment to collect real-time data. The sensors are configured to measure specific process parameters, such as temperature, pressure, or flow rate, and transmit the observed values to a computer device for analysis. The computer device processes the collected data to detect deviations from predefined thresholds or patterns, triggering automated adjustments to the process or generating alerts for operator intervention. The embedded sensors are designed to withstand harsh industrial conditions, ensuring reliable data acquisition. The system integrates with existing process control infrastructure, allowing seamless integration into manufacturing, chemical processing, or energy production environments. By continuously monitoring and adjusting process variables, the system improves efficiency, reduces waste, and enhances product quality. The method ensures real-time data transmission, enabling prompt decision-making and minimizing downtime. The system is particularly useful in industries where precise control of process parameters is critical, such as semiconductor fabrication, pharmaceutical production, or oil refining. The embedded sensors may include wireless or wired communication capabilities, depending on the application requirements. The computer device may employ machine learning algorithms to predict potential process failures or optimize operational parameters dynamically. The system provides a scalable solution, adaptable to various industrial scales and process complexities.
3. The method of claim 2 , wherein the observed value is one of a set of observed values in a stream of observed values collected by the embedded sensor in real time.
This invention relates to real-time data collection and processing using embedded sensors. The problem addressed is efficiently managing and analyzing continuous data streams from sensors to extract meaningful insights without overwhelming computational resources. The system involves an embedded sensor that collects a stream of observed values in real time. These values are part of a set of observed values, meaning the sensor continuously generates data points over time. The method processes these values to detect patterns, anomalies, or other relevant information. The sensor may be part of a larger monitoring system, such as in industrial equipment, environmental monitoring, or IoT devices, where real-time analysis is critical for decision-making or predictive maintenance. The embedded sensor captures data points sequentially, forming a continuous stream. The method may include filtering, aggregating, or analyzing these values to reduce noise or extract trends. For example, in industrial applications, the sensor could monitor vibration levels in machinery, with the stream of observed values helping detect early signs of wear or failure. The real-time aspect ensures timely responses, such as triggering alerts or adjustments to prevent downtime. The invention improves upon traditional batch processing by handling data as it arrives, reducing latency and enabling immediate actions. This is particularly useful in environments where delays could lead to inefficiencies or safety risks. The method may also include adaptive algorithms that adjust processing based on the nature of the observed values, ensuring optimal performance under varying conditions.
4. The method of claim 1 , wherein the adjusting and readjusting of the set of coefficients is performed using a quasi-Newton method.
This invention relates to optimizing a set of coefficients in a computational model using a quasi-Newton method. The method addresses the challenge of efficiently refining coefficients in iterative optimization processes, particularly where traditional gradient-based techniques may be computationally expensive or impractical. The quasi-Newton approach approximates the Hessian matrix, reducing the need for second-order derivatives while maintaining convergence efficiency. The method involves iteratively adjusting and readjusting the coefficients based on gradient information and updates to the Hessian approximation. This technique is particularly useful in machine learning, signal processing, and other fields requiring iterative optimization of model parameters. The quasi-Newton method balances computational efficiency and accuracy, making it suitable for large-scale or high-dimensional optimization problems. The invention may be applied to training neural networks, solving nonlinear least-squares problems, or optimizing control systems. By leveraging quasi-Newton updates, the method avoids the computational overhead of exact Hessian calculations while still achieving rapid convergence. The approach is adaptable to various optimization scenarios, including constrained and unconstrained problems, and can be combined with other optimization techniques for enhanced performance.
5. The method of claim 1 , wherein a friction component is adjusted during the adjusting and readjusting of the set of coefficients, wherein the friction component is due to a physical limitation of the complex flow system at the node, wherein the physical limitation obstructs passage of the substance through the complex flow system at the node.
This invention relates to optimizing flow control in complex flow systems, such as those used in industrial processes, where physical limitations create friction that obstructs the passage of substances. The method involves adjusting a set of coefficients to regulate flow through the system, with a key feature being the dynamic adjustment of a friction component during this process. The friction component represents a physical limitation at a specific node in the system, such as a valve, pipe restriction, or other obstruction, that impedes flow. By accounting for and adjusting this friction component during the coefficient optimization, the method improves flow efficiency and reduces energy loss. The system may include multiple nodes, each with its own friction characteristics, and the method iteratively readjusts the coefficients to balance flow rates while compensating for these physical constraints. This approach ensures smoother operation and better performance in systems where flow resistance varies due to structural or environmental factors. The invention is particularly useful in applications where precise flow control is critical, such as chemical processing, water treatment, or HVAC systems.
6. The method of claim 1 , further comprising: making a comparison between the output from the model and an observed value of a second node of a plurality of nodes in the complex flow system; computing a second adjoint sensitivity for the second node based on the comparison; and adjusting a set of coefficients of the model associated with the second node based on the second adjoint sensitivity.
This invention relates to optimizing models of complex flow systems, such as those used in fluid dynamics, thermodynamics, or chemical processes. The problem addressed is the difficulty in accurately predicting system behavior due to uncertainties in model parameters and boundary conditions. Traditional methods often fail to efficiently adjust model coefficients to minimize errors across multiple nodes in the system. The method involves using an adjoint-based sensitivity analysis to iteratively refine a computational model. Initially, a model is trained to predict the behavior of a first node in the system by comparing its output to an observed value. An adjoint sensitivity is computed to quantify how changes in model coefficients affect the prediction error. The coefficients are then adjusted to minimize this error. The method further extends this process to additional nodes in the system. For each subsequent node, the model's output is compared to an observed value, and a second adjoint sensitivity is computed. The model coefficients associated with that node are adjusted based on this sensitivity. This iterative approach ensures that the model is optimized across multiple points in the system, improving overall accuracy. The technique is particularly useful in large-scale simulations where manual tuning is impractical.
7. A system for calibrating a complex flow system model configured to characterize a flow of a substance within the complex flow system, comprising at least one computer device configured to perform a method comprising: making a comparison between an output from the model and an observed value for a node of a plurality of nodes located in the complex flow system, wherein each of the plurality of nodes are points in the complex flow system at which a measurement of the substance is determined within the model; computing an adjoint sensitivity for the node based on the comparison; and adjusting a set of coefficients of the model associated with the node based on the adjoint sensitivity.
This invention relates to calibrating complex flow system models used to characterize the flow of substances within such systems. The problem addressed is the need for accurate and efficient calibration of these models to ensure they reliably predict flow behavior based on real-world measurements. The system involves a computer device that performs a calibration method by comparing model outputs with observed values at specific nodes within the flow system. These nodes are points where measurements of the substance are taken. The system computes an adjoint sensitivity for each node based on the comparison between the model's predicted output and the observed value. Using this adjoint sensitivity, the system adjusts a set of coefficients in the model that are associated with the node. This iterative process improves the model's accuracy by refining its parameters to better match real-world observations. The method leverages adjoint-based sensitivity analysis, which is a mathematical technique for efficiently computing gradients of model outputs with respect to input parameters, enabling precise and computationally efficient calibration. The system is designed to handle the complexity of flow systems, where multiple interacting factors influence the flow behavior, and ensures that the model remains accurate over time as new data becomes available.
8. The system of claim 7 , wherein the at least one computer device is configured to compute the adjoint sensitivity for a plurality of nodes simultaneously.
The invention relates to computational systems for optimizing fluid flow simulations, particularly in applications like aerodynamics or fluid dynamics. The problem addressed is the computational inefficiency in calculating adjoint sensitivities, which are critical for optimizing designs but traditionally require significant processing time and resources. The system includes at least one computer device configured to perform these calculations more efficiently by computing adjoint sensitivities for multiple nodes simultaneously. This parallel processing approach reduces the overall computation time and resource usage compared to sequential methods. The system may also include a memory for storing simulation data and a network interface for communicating with other computing devices, enabling distributed processing. The simultaneous computation of adjoint sensitivities allows for faster design iterations, making the system particularly useful in industries where rapid prototyping and optimization are essential, such as aerospace, automotive, and energy sectors. The invention improves upon prior art by leveraging parallel processing to enhance computational efficiency without sacrificing accuracy.
9. The system of claim 7 , wherein the observed value is collected from an embedded sensor within the complex flow system, wherein the embedded sensor is configured to collect the observed value for transmission to the computer device.
The invention relates to monitoring and controlling complex flow systems, such as those used in industrial processes, where precise regulation of fluid flow is critical. A key challenge in such systems is accurately measuring and adjusting flow parameters to maintain optimal performance, often complicated by environmental factors, sensor inaccuracies, or system dynamics. The invention addresses this by integrating an embedded sensor within the flow system to collect real-time observed values of flow characteristics. These values are transmitted to a computer device, which processes the data to generate control signals for adjusting the system. The embedded sensor is specifically designed to operate within the flow system, ensuring direct and reliable measurement of flow parameters. The computer device uses this data to implement feedback control, dynamically adjusting system components to maintain desired flow conditions. This approach improves accuracy and responsiveness compared to traditional methods that rely on external sensors or manual adjustments. The system is particularly useful in applications requiring high precision, such as chemical processing, water treatment, or oil and gas flow management, where deviations in flow can lead to inefficiencies or safety hazards. By embedding the sensor directly within the system, the invention minimizes measurement delays and enhances overall control performance.
10. The system of claim 7 , wherein the adjusting and readjusting of the set of coefficients is performed using a quasi-Newton method.
This invention relates to an adaptive signal processing system designed to optimize signal quality by dynamically adjusting a set of coefficients. The system addresses the challenge of maintaining accurate signal processing in environments where signal characteristics change over time, such as in communication systems, noise cancellation, or sensor data processing. The core functionality involves iteratively adjusting and readjusting a set of coefficients to minimize error or maximize performance metrics, such as signal-to-noise ratio or convergence speed. The system includes a coefficient adjustment module that processes input signals and computes error metrics to guide the adjustment process. A feedback loop ensures continuous refinement of the coefficients based on real-time performance data. The adjustment process is governed by a quasi-Newton method, an optimization technique that approximates the Hessian matrix to efficiently navigate the error surface without requiring explicit second-order derivatives. This method balances computational efficiency and accuracy, making it suitable for real-time applications. The system may also incorporate additional features, such as adaptive learning rates or regularization techniques, to enhance stability and convergence. The quasi-Newton method allows the system to adapt to varying signal conditions without prior knowledge of the underlying signal model, making it versatile for diverse applications. The overall goal is to achieve robust and efficient signal processing by dynamically optimizing the coefficient set in response to changing input conditions.
11. The system of claim 7 , wherein a friction component is adjusted during the adjusting and readjusting of the set of coefficients, wherein the friction component is due to a physical limitation of the complex flow system at the node, wherein the physical limitation obstructs passage of the substance through the complex flow system at the node.
This invention relates to systems for managing flow in complex fluid or particulate transport networks, such as pipelines, where physical limitations at nodes (e.g., valves, junctions, or obstructions) create friction that impedes substance passage. The system dynamically adjusts a set of control coefficients to optimize flow while accounting for these friction components. The friction component represents a physical constraint at a node, such as resistance due to blockages, valve settings, or other flow restrictions. During operation, the system continuously readjusts the coefficients to compensate for these limitations, ensuring efficient flow despite the inherent friction. The adjustment process may involve real-time monitoring of flow conditions and iterative recalibration of control parameters to maintain desired throughput. This approach improves system performance by dynamically adapting to varying physical constraints, reducing inefficiencies caused by friction-induced obstructions. The invention is particularly useful in industrial applications where precise flow control is critical, such as chemical processing, oil and gas transport, or water distribution systems.
12. The system of claim 7 , the method further comprising: making a comparison between the output from the model and an observed value of a second node of a plurality of nodes in the complex flow system; computing a second adjoint sensitivity for the second node based on the comparison; and adjusting a set of coefficients of the model associated with the second node based on the second adjoint sensitivity.
This invention relates to optimizing models of complex flow systems, such as those used in fluid dynamics, thermodynamics, or chemical processes. The problem addressed is the difficulty in accurately modeling and adjusting system behavior when multiple interconnected nodes influence the overall flow dynamics. Traditional modeling approaches often fail to account for the sensitivity of individual nodes to changes in system parameters, leading to inaccuracies in predictions and control. The system includes a model representing the complex flow system, where the model is defined by a set of coefficients that govern the behavior of various nodes within the system. The model generates an output representing the predicted behavior of a first node. The system compares this output to an observed value of the first node and computes an adjoint sensitivity—a measure of how changes in the model coefficients affect the node's behavior. Based on this sensitivity, the system adjusts the coefficients to improve the model's accuracy for the first node. Additionally, the system extends this process to a second node. It compares the model's output for the second node to its observed value, computes a second adjoint sensitivity, and adjusts the model coefficients accordingly. This iterative adjustment ensures that the model accurately reflects the behavior of multiple nodes, improving overall system performance. The method allows for real-time or near-real-time adjustments, enhancing the model's reliability in dynamic environments.
13. A method for deploying an application for calibrating a complex flow system model in which a substance flows from one point to another, comprising: providing a computer infrastructure configured to: collect by an embedded sensor an observed value for a node of a plurality of nodes in the complex flow system, wherein each of the plurality of nodes are points in the complex flow system at which a measurement of the substance is determined within the model; make a comparison between an output from the model and an observed value for a node located in the complex flow system, wherein the output and the observed value represent a parameter that affects flow of the substance within the complex flow system at the node; compute an adjoint sensitivity for the node based on the comparison; and adjust a coefficient of the model associated with the node based on the adjoint sensitivity in order to bring the output from the model closer to the observed value; and providing a sensor embedded within the complex flow system at the node, wherein the sensor is configured to collect the observed value in real time, wherein a cross-section of the node is defined by a physical limitation of the complex flow system located at the node, wherein the physical limitation defines a capacity of the complex flow system to pass the substance as it moves through the complex flow system at the node.
This invention relates to a method for calibrating complex flow system models, such as those used in fluid dynamics, pipeline networks, or environmental modeling, where a substance flows between multiple points. The problem addressed is the need to accurately adjust model parameters to match real-world observations, improving predictive accuracy. The method involves a computer infrastructure that collects observed values from embedded sensors at various nodes within the system. Each node represents a point where measurements of the substance are taken. The system compares model outputs with observed values at these nodes, focusing on parameters that influence flow, such as pressure, velocity, or concentration. Using adjoint sensitivity analysis, the method computes adjustments to model coefficients to minimize discrepancies between simulated and observed data. These adjustments are applied in real time, ensuring continuous model refinement. The embedded sensors are strategically placed at nodes defined by physical constraints, such as pipe cross-sections or flow restrictions, which limit the system's capacity. By integrating real-time sensor data, the method dynamically calibrates the model, enhancing its accuracy in representing the flow system's behavior. This approach is particularly useful in industries like oil and gas, water distribution, or chemical processing, where precise flow modeling is critical.
14. The method of claim 13 , wherein a friction component is adjusted during the adjusting and readjusting of the coefficient, wherein the friction component is due to the physical limitation of the complex flow system at the node, wherein the physical limitation obstructs passage of the substance through the complex flow system at the node.
This invention relates to optimizing flow control in complex flow systems, such as those used in chemical processing, fluid dynamics, or industrial automation, where physical limitations at nodes (e.g., valves, pipes, or junctions) create friction that obstructs the passage of substances. The problem addressed is the inefficient adjustment of flow coefficients in such systems, which can lead to suboptimal performance, energy waste, or system damage. The method involves dynamically adjusting a friction component during the iterative adjustment and readjustment of a flow coefficient. The friction component arises from physical constraints at a node, such as mechanical resistance, turbulence, or blockages, which impede the smooth flow of substances. By accounting for and modifying this friction component in real-time, the system can achieve more precise and efficient flow control. This adjustment may involve altering operational parameters, such as pressure, temperature, or valve positions, to compensate for the friction and ensure the desired flow rate is maintained. The method ensures that the system adapts to changing conditions, improving overall efficiency and reliability.
15. A system comprising: a complex flow system in which a substance flows from one point to another, wherein the complex flow system contains a plurality of nodes; a computer device configured to calibrate a model of the complex flow system, wherein the model is configured to characterize the flow of the substance within the complex flow system, wherein the model comprises: a model to observation comparison component configured to make a comparison between an output from the model and an observed value for a node of a plurality of nodes located in the complex flow system; an adjoint sensitivity computing component configured to compute an adjoint sensitivity for the node based on the comparison; and a coefficient adjusting component configured to adjust a set of coefficients of the model associated with the node based on the adjoint sensitivity; and an embedded sensor located in proximity to the node, wherein the embedded sensor is configured to collect the observed value for transmission to the computer device.
This invention relates to a system for modeling and optimizing the flow of substances within complex flow systems, such as pipelines, networks, or industrial processes. The system addresses the challenge of accurately characterizing and adjusting flow dynamics in systems with multiple interconnected nodes, where traditional modeling approaches may fail to account for real-time variations or uncertainties. The system includes a complex flow system with multiple nodes through which a substance flows. A computer device is used to calibrate a model of this system, which predicts the flow behavior. The model features three key components: a model-to-observation comparison component that compares the model's predicted output with actual observed values from the system; an adjoint sensitivity computing component that calculates how sensitive the model's output is to changes in its parameters (adjoint sensitivity) based on this comparison; and a coefficient adjusting component that updates the model's coefficients to improve accuracy. Embedded sensors near each node collect real-time observed values and transmit them to the computer device for calibration. This adaptive approach ensures the model remains accurate despite variations in flow conditions, improving system efficiency and reliability.
16. The system of claim 15 , further comprising: the model to observation comparison component further configured to make a comparison between the output from the model and an observed value of a second node of a plurality of nodes in the complex flow system; the adjoint sensitivity computing component further configured to compute a second adjoint sensitivity for the second node based on the comparison; and the coefficient adjusting component further configured to adjust a set of coefficients of the model associated with the second node based on the second adjoint sensitivity.
This invention relates to a system for optimizing models of complex flow systems, such as those used in engineering or scientific simulations. The problem addressed is the difficulty in accurately modeling dynamic systems with multiple interconnected nodes, where small errors in one part of the system can propagate and lead to significant inaccuracies. The system improves model accuracy by iteratively comparing model outputs to observed values and adjusting model coefficients based on adjoint sensitivity calculations. The system includes a model-to-observation comparison component that evaluates discrepancies between the model's predicted values and actual measurements from nodes within the system. An adjoint sensitivity computing component calculates sensitivity values, which quantify how changes in model parameters affect the model's output. These sensitivities guide adjustments to the model's coefficients, improving its predictive accuracy. The system is designed to handle multiple nodes, allowing for localized adjustments based on observed data from different parts of the system. By iteratively refining the model using adjoint-based optimization, the system enhances the reliability of simulations in complex flow systems, such as those encountered in fluid dynamics, thermodynamics, or other engineering applications.
17. The system of claim 15 , wherein a cross-section of the node is defined by physical limits of the complex flow system located at the node.
The invention relates to a system for managing fluid flow in a complex flow system, such as a network of pipes, channels, or conduits. The system addresses the challenge of accurately modeling and controlling fluid dynamics at critical points, or nodes, within the system where flow characteristics change significantly. These nodes may include junctions, valves, pumps, or other components where fluid behavior is influenced by physical constraints. The system includes a node with a cross-section that is defined by the physical limits of the complex flow system at that node. This means the node's geometry and boundaries are determined by the surrounding infrastructure, such as pipe diameters, wall thicknesses, or other structural features. By defining the node's cross-section in this way, the system ensures that fluid flow simulations and control strategies account for real-world physical constraints, improving accuracy and performance. The system may also incorporate sensors to monitor flow parameters, such as pressure, velocity, or temperature, at the node. These measurements are used to adjust flow control mechanisms, such as valves or pumps, to optimize fluid distribution or prevent issues like blockages or leaks. Additionally, the system may include a computational model that simulates fluid behavior at the node, allowing for predictive maintenance or dynamic adjustments to flow conditions. By integrating physical constraints into the node's design and operation, the system enhances the reliability and efficiency of fluid management in complex flow systems. This approach is particularly useful in industrial, municipal, or environmental applications where precise control of fluid flow is essential.
18. The system of claim 17 , wherein the physical limits define a capacity of the complex flow system to pass the substance as it moves through the complex flow system at the node.
The invention relates to a system for managing the flow of substances within a complex flow system, such as a network of pipes, channels, or conduits. The system addresses the challenge of optimizing flow efficiency while ensuring operational constraints are met, particularly at critical nodes where flow dynamics are complex. The system includes a computational model that simulates the behavior of the substance as it moves through the system, accounting for physical limits such as pressure, temperature, or flow rate at each node. These physical limits define the capacity of the system to pass the substance at that node, ensuring safe and efficient operation. The system dynamically adjusts flow parameters based on real-time data to prevent bottlenecks or failures. Additionally, the system may incorporate predictive analytics to anticipate changes in flow conditions and proactively adjust operations. The invention improves system reliability, reduces downtime, and enhances overall performance by maintaining flow within defined physical limits.
19. The system of claim 18 , wherein the physical limits include an obstruction that inhibits passage of the substance through the complex flow system at the node.
This invention relates to a system for managing fluid or substance flow within a complex flow system, such as a network of pipes, channels, or conduits. The system is designed to address challenges in monitoring and controlling the movement of substances through interconnected pathways, particularly when obstructions or physical constraints affect flow dynamics at specific nodes. The system includes sensors and control mechanisms that detect and respond to physical limits within the flow system. These limits may include obstructions, blockages, or other conditions that restrict or inhibit the passage of substances through the system at a particular node. The system dynamically adjusts flow parameters, such as pressure, velocity, or direction, to mitigate the impact of these obstructions and maintain efficient substance transport. It may also include predictive modeling to anticipate flow disruptions and preemptively adjust system operations. The system may further incorporate feedback loops that continuously monitor flow conditions and adjust control parameters in real time. This ensures optimal performance even when physical constraints vary over time. The invention is particularly useful in industrial, medical, or environmental applications where precise control of fluid or substance flow is critical.
20. The system of claim 18 , wherein the sensor gathers the observed value of the substance as it passes through the complex flow system at the node.
A system for monitoring substances in a complex flow system involves a sensor that measures the observed value of a substance as it moves through the system at a specific node. The system includes a flow path with multiple nodes, where each node represents a point where the substance can be measured. The sensor is positioned at one of these nodes to detect and record the observed value of the substance, such as concentration, pressure, temperature, or flow rate. The system may also include a processing unit that analyzes the observed values to determine the substance's behavior, detect anomalies, or optimize flow conditions. The sensor can be calibrated to ensure accurate measurements, and the system may integrate with other monitoring or control devices to adjust flow parameters based on the observed data. This approach enables real-time tracking of substances in dynamic flow environments, improving efficiency and safety in industrial, chemical, or environmental applications.
21. The system of claim 20 , wherein the complex flow system is an electrical distribution system, and the substance is electricity.
This invention relates to monitoring and managing complex flow systems, particularly electrical distribution systems, to optimize performance and detect anomalies. The system includes sensors distributed throughout the electrical distribution network to measure parameters such as voltage, current, and power quality. These sensors collect real-time data, which is transmitted to a central processing unit for analysis. The system uses advanced algorithms to detect deviations from expected behavior, such as voltage fluctuations, current imbalances, or power quality issues, indicating potential faults or inefficiencies. The system also includes a user interface that visualizes the data and alerts operators to critical conditions, enabling proactive maintenance and reducing downtime. Additionally, the system may integrate with predictive models to forecast future performance based on historical and real-time data, allowing for preemptive adjustments. The invention aims to improve the reliability, efficiency, and safety of electrical distribution networks by providing continuous monitoring and intelligent analysis of electrical flow.
22. The system of claim 20 , wherein the complex flow system is a wind flow system, and the substance is wind.
This invention relates to a system for analyzing and managing wind flow within a complex flow system. The system is designed to address challenges in monitoring and controlling wind patterns, particularly in environments where wind behavior is influenced by multiple interacting factors such as terrain, obstacles, or structural elements. The system includes sensors and computational components to measure and model wind flow characteristics, such as velocity, direction, and turbulence, in real-time. By processing this data, the system can optimize wind flow for applications like energy generation, ventilation, or environmental monitoring. The system may also incorporate adaptive mechanisms to adjust flow conditions dynamically, ensuring efficient and safe operation. The invention focuses on improving the predictability and controllability of wind flow in complex environments, enhancing performance and reliability in wind-related applications.
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May 26, 2020
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